Add model artifact and model card with inference metadata

README.md

@@ -23,42 +23,3 @@ merge_mode: weighted_average
 
 # Model Card
 
-Exported using the following code:
-
-```python
-from pathlib import Path
-import torch
-import torchvision.transforms.v2 as T
-from stac_model.torch.export import save
-import segmentation_models_pytorch as smp
-
-
-path = "FTW-Release-Full-3-class-unet-efficientnetb5-weight0.75-3xlonger.ckpt"
-ckpt = torch.load(path, map_location="cpu", weights_only=False)
-hparams = ckpt["hyper_parameters"]
-state_dict = {k.replace("model.", ""): v for k, v in ckpt["state_dict"].items()}
-del state_dict["criterion.weight"]
-model = smp.Unet(
-    encoder_name=hparams["backbone"],
-    encoder_weights=None,
-    in_channels=hparams["in_channels"],
-    classes=hparams["num_classes"],
-)
-model.load_state_dict(state_dict, strict=True)
-
-transforms = torch.nn.Sequential(
-    torch.nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False),
-    T.Normalize(mean=[0.0], std=[3000.0])
-)
-
-save(
-    output_file=Path("model.pt2"),
-    input_shape=[-1, hparams["in_channels"], -1, -1],
-    model=model,
-    transforms=transforms,
-    metadata=None,
-    device="cpu",
-    dtype=torch.float32,
-    aoti_compile_and_package=False
-)
-```

ftw-v1.1-ep.pt2

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 134509081
+oid sha256:ab3ca1126a3d58e2120f154a8088f7df305efeb982b03639ff14cc34d8deebf1
+size 134606489